def select_images_loader(src_data_type, src_data_path): if src_data_type == "video": images_loader = lib_images_io.ReadFromVideo( src_data_path, sample_interval=SRC_VIDEO_SAMPLE_INTERVAL) elif src_data_type == "folder": images_loader = lib_images_io.ReadFromFolder(folder_path=src_data_path) elif src_data_type == "webcam": if src_data_path == "": webcam_idx = 0 elif src_data_path.isdigit(): webcam_idx = int(src_data_path) else: webcam_idx = src_data_path images_loader = lib_images_io.ReadFromWebcam(SRC_WEBCAM_MAX_FPS, webcam_idx) return images_loader
return None if id == 'min': id = min(self.dict_id2clf.keys()) return self.dict_id2clf[id] # -- Main if __name__ == "__main__": skeleton_detector = SkeletonDetector(OPENPOSE_MODEL, OPENPOSE_IMG_SIZE) multiperson_tracker = Tracker() multiperson_classifier = MultiPersonClassifier("/home/yitao/Documents/fun-project/tensorflow-related/Realtime-Action-Recognition/model/trained_classifier.pickle", ['stand', 'walk', 'run', 'jump', 'sit', 'squat', 'kick', 'punch', 'wave']) # images_loader = select_images_loader(SRC_DATA_TYPE, SRC_DATA_PATH) images_loader = lib_images_io.ReadFromVideo("/home/yitao/Documents/fun-project/tensorflow-related/Realtime-Action-Recognition/data_test/exercise.avi", sample_interval = 1) frame_id = 1 while (frame_id < 10): print("Processing %dth image" % frame_id) img = images_loader.read_image() humans = skeleton_detector.detect(img) skeletons, scale_h = skeleton_detector.humans_to_skels_list(humans) skeletons = remove_skeletons_with_few_joints(skeletons) # print("[Yitao] len(skeletons) = %s" % len(skeletons)) # print(skeletons[0]) dict_id2skeleton = multiperson_tracker.track(skeletons)
def predict(request, time=None): if time not in run_code.keys(): run_code[time] = True if request.method == 'POST' and request.FILES: upload = UploadForm(request.POST, request.FILES) if upload.is_valid(): videoinput = request.FILES['file'] handle_uploaded_file(videoinput) else: upload = UploadForm() return render(request, "index.html", {'form': upload}) # def get_folder_name(data_type): # ''' # 根據data_type和data_path計算輸出文件夾名稱。 # 該腳本的最終輸出如下所示: # DST_FOLDER/folder_name/video.avi # DST_FOLDER/folder_name/skeletons/XXXXX.txt # ''' # if data_type == "video": # /root/data/video.avi --> video # folder_name = videoinput.name.split(".")[-2] # return folder_name DATA_TYPE = "video" DATA_PATH = f"{CURR_PATH}/static/upload/{videoinput.name}" MODEL_PATH = f"{ROOT}/model/dnn_model.h5" # DST_FOLDER_NAME = get_folder_name(DATA_TYPE) DST_FOLDER_NAME = videoinput.name.split(".")[-2] output_folder = f"{ROOT}/output" # ----設定 cfg_all = lib_commons.read_yaml(ROOT + "config/config.yaml") cfg = cfg_all["s5_test.py"] CLASSES = np.array(cfg_all["classes"]) # "{:05d}.txt" SKELETON_FILENAME_FORMAT = cfg_all["skeleton_filename_format"] # 動作識別:用於提取特徵的幀數。 WINDOW_SIZE = int(cfg_all["features"]["window_size"]) # 5 # Output folder DST_FOLDER = output_folder + "/" + DST_FOLDER_NAME + "/" DST_SKELETON_FOLDER_NAME = cfg["output"]["skeleton_folder_name"] DST_VIDEO_NAME = cfg["output"]["video_name"] # 輸出video.avi的幀率 DST_VIDEO_FPS = float(cfg["output"]["video_fps"]) # Video 設定 # 如果data_type為video,則設置採樣間隔。 # 例如,如果為3,則video的讀取速度將提高3倍。 VIDEO_SAMPLE_INTERVAL = int( cfg["settings"]["source"]["video_sample_interval"]) # Openpose 設定 OPENPOSE_MODEL = cfg["settings"]["openpose"]["model"] # cmu OPENPOSE_IMG_SIZE = cfg["settings"]["openpose"]["img_size"] # 656x368 # Display 設定 img_disp_desired_rows = int( cfg["settings"]["display"]["desired_rows"]) # 480 # -- Detector, tracker, classifier skeleton_detector = SkeletonDetector(OPENPOSE_MODEL, OPENPOSE_IMG_SIZE) multiperson_tracker = Tracker() multiperson_classifier = MultiPersonClassifier(MODEL_PATH, CLASSES, WINDOW_SIZE) # -- Image reader and displayer images_loader = lib_images_io.ReadFromVideo( DATA_PATH, sample_interval=VIDEO_SAMPLE_INTERVAL) # 網頁上不顯示 # img_displayer = lib_images_io.ImageDisplayer() # cv2.namedWindow('cv2_display_window', cv2.WINDOW_NORMAL) # cv2.resizeWindow('cv2_display_window',570,460) # cv2.moveWindow("cv2_display_window", 900, 275) # -- Init output # output folder os.makedirs(DST_FOLDER, exist_ok=True) os.makedirs(DST_FOLDER + DST_SKELETON_FOLDER_NAME, exist_ok=True) # video writer # video_writer = lib_images_io.VideoWriter( # DST_FOLDER + DST_VIDEO_NAME, DST_VIDEO_FPS) # -- Read images and process try: ith_img = -1 while images_loader.has_image() and run_code[time]: # -- Read image img = images_loader.read_image() ith_img += 1 img_disp = img.copy() print(f"\nProcessing {ith_img}th image ...") # -- Detect skeletons humans = skeleton_detector.detect(img) skeletons, scale_h = skeleton_detector.humans_to_skels_list( humans) # skeletons shape : (1, 36) # -- Track people # dict_id2skeleton => {id : skeletons} dict_id2skeleton = multiperson_tracker.track(skeletons) # -- Recognize action of each person if len(dict_id2skeleton): dict_id2label = multiperson_classifier.classify( dict_id2skeleton) # -- Draw img_disp = draw_result_img(img_disp, ith_img, humans, dict_id2skeleton, skeleton_detector, multiperson_classifier, img_disp_desired_rows, dict_id2label, scale_h) # 將BGR照片轉為RGB img_rgb = cv2.cvtColor(img_disp, cv2.COLOR_BGR2RGB) # -- Display image, and write to video.avi # 網頁上不顯示 # img_displayer.display(img_disp, wait_key_ms=1) global img_tmp_dict img_tmp_dict[time] = img_rgb # video_writer.write(img_disp) # -- Get skeleton data and save to file skels_to_save = get_the_skeleton_data_to_save_to_disk( dict_id2skeleton, dict_id2label) result = np.array(skels_to_save) global list_result list_result = {i + 1: result[i, 1] for i in range(result.shape[0])} print(list_result) # save the result to txt files for all frame lib_commons.save_listlist( DST_FOLDER + DST_SKELETON_FOLDER_NAME + SKELETON_FILENAME_FORMAT.format(ith_img), skels_to_save) # global dict_result # global status_a # global status_h # global status_o # for uid, action in list_result.items(): # if uid not in dict_result.keys(): # dict_result[uid] = [] # dict_result[uid].append(action) # else: # if len(dict_result[uid]) > 15: # dict_result[uid].pop(0) # dict_result[uid].append(action) # else: # dict_result[uid].append(action) # count_kick = dict_result[uid].count("kick") # count_punch = dict_result[uid].count("punch") # count_sos = dict_result[uid].count("sos") # count_opendoor = dict_result[uid].count("opendoor") # if count_kick > 10 or count_punch > 10: # status_a = True # lineNotifyMessage(msg="發現疑似違規行為") # dict_result[uid].clear() # else : # status_a = False # if count_sos > 10: # status_h = True # lineNotifyMessage(msg="有人發出求救訊號") # dict_result[uid].clear() # else : # status_h = False # if count_opendoor > 10: # status_o = True # dict_result[uid].clear() # lineNotifyMessage(msg="請檢查隨身物品是否攜帶") # else : # status_o = False return render(request, "index.html", {'form': upload}) except Exception as e: print(e) finally: # video_writer.stop() print("Program ends") list_result = None